Print Close

Alterations of functional connectivity within the DMN of military Veterans with suicidal behavior

Poster No:

678

Submission Type:

Abstract Submission

Authors:

Jadwiga Rogowska¹, Perry Renshaw¹^,2^,3, Deborah Yurgelun-Todd¹^,2^,3, Erin McGlade¹^,2^,3

Institutions:

¹Diagnostic Neuroimaging Lab, University of Utah, Salt Lake City, UT, ²Huntsman Mental Health Institute, University of Utah, Salt Lake City, UT, ³MIRECC, Department of Veterans Affairs, Salt Lake City, UT

First Author:

Jadwiga Rogowska, Dr
Diagnostic Neuroimaging Lab, University of Utah
Salt Lake City, UT

Co-Author(s):

Perry Renshaw
Diagnostic Neuroimaging Lab, University of Utah|Huntsman Mental Health Institute, University of Utah|MIRECC, Department of Veterans Affairs
Salt Lake City, UT|Salt Lake City, UT|Salt Lake City, UT

Deborah Yurgelun-Todd
Diagnostic Neuroimaging Lab, University of Utah|Huntsman Mental Health Institute, University of Utah|MIRECC, Department of Veterans Affairs
Salt Lake City, UT|Salt Lake City, UT|Salt Lake City, UT

Erin McGlade
Diagnostic Neuroimaging Lab, University of Utah|Huntsman Mental Health Institute, University of Utah|MIRECC, Department of Veterans Affairs
Salt Lake City, UT|Salt Lake City, UT|Salt Lake City, UT

Introduction:

In recent years, there has been an increased focus on suicide prevention for the American Veteran population [1-2]. Although a range of studies has been undertaken to identify potential risk factors, self-directed violence remains difficult to predict [3-4]. Anomalous functioning of the default mode network (DMN) has been associated with impairment in an individual's ability to monitor and inhibit behavior, and therefore may lead to the initiation of high-risk behavior such as suicide [5]. However, functional connectivity of DMN, especially among suicidal veterans, is not well documented. Therefore, the aim of the current study was to use resting state functional magnetic resonance imaging (rsFMRI) and functional connectivity (FC) of the DMN to identify and visualize brain changes in Veterans with suicide attempts as well as those with a history limited to suicidal ideation.

Methods:

Seventy-eight Veterans (62 males, 16 females; mean age: 36.6) completed the demographic and clinical measures and an 8-min resting state fMRI on a 3T scanner. Participants included a suicidal ideation (SI) group (N= 30), a suicide attempt (SA) group (N=22) and a healthy control (HC) group, consisting of Veterans without a history of suicidal ideation or attempts (N=26). Participants completed the Columbia-Suicide Severity Rating Scale (C-SSRS). Image data were motion corrected, normalized and smoothed using DPARSFA and SPM8 [6]. To further reduce motion-related artifacts, the data were "scrubbed" [7]. The FC maps were computed by using a standard seed-based whole brain correlation method with posterior cingulate cortex (PCC) as a seed region. One-sample t-tests were done to determine brain regions showing significant functional connectivity in the DMN (p<.005, FDRcorr). Factorial analyses controlling for both age and sex were performed for the three groups (HC, SI, SA) with group as an effect. Post-hoc analyses were then performed between participant groups (p<0.05, FWEcorr, k >200).

Results:

The SA data as compared with HC data showed stronger DMN connectivity to the calcarine, cerebellum, right regions of superior occipital, lingual, fusiform, cuneus and inferior occipital (k=708, p=0.003), and right regions of insula, supramarginal, inferior frontal, precentral, postcentral, putamen and rolandic operand (k=429, p=0.038) (Fig. 1). In addition, SA group as compared with the SI group, demonstrated stronger DMN connectivity to the left precentral, postcentral, middle cingulate, inferior and middle temporal, insula and left and right precuneus (k=1254, p<0.0001), as well as to the right regions of precentral, insula, hippocampus, caudate and palladium (k=499, p=0.015) and superior and orbital frontal regions (k=418, p=0.027) (Fig. 2). All other group comparisons for DMN connectivity did not show any statistically significant differences.

Conclusions:

There is a growing interest in understanding suicidal behavior using resting state connectivity [8-10]. Our results indicate that the DMN is hyperconnected to multiple brain areas in Veterans with suicide ideation and suicide attempts, reflecting highly synchronized brain circuitry at rest. Differences in connectivity between the SA and SI and between the SA and HC groups were identified and suggest that suicidal behavior in Veterans may be uniquely related to abnormal functional connectivity in the default mode network. These differences could be a potential biomarker in suicide behavior prediction as well as focal targets for intervention. Additional studies controlling for severity of behaviors, psychiatric diagnosis and medication are needed to further clarify brain changes related to suicide behaviors.

·Figure 1. Functional connectivity in DMN demonstrates greater activation in SA relative to HC (p<0.05 and cluster size k>200).

·Figure 2. Functional connectivity DMN demonstrates greater activation in SA relative to SI (p<0.05 and cluster size k>200).

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia) ¹

Modeling and Analysis Methods:

fMRI Connectivity and Network Modeling ²

Keywords:

FUNCTIONAL MRI

Other - Resting state, functional connectivity, suicide

^1|2Indicates the priority used for review

Provide references using author date format

1. Kaplan, M.S. (2007), ‘Suicide among male veterans: a prospective population-based study’, J Epidemiol Community Health, vol. 61, pp. 619-24.
2. Pompili, M. (2013), ‘Posttraumatic stress disorder and suicide risk among veterans: a literature review’, The Journal of Nervous and Mental Disease, vol. 201, pp. 802-812.
3. Deshpande, G. (2016), ‘A Neural Basis for the Acquired Capability for Suicide’, Frontiers in Psychiatry, vol. 7, article 125.
4. van Heeringen, K. (2014). ‘The neurobiology of suicide’, Lancet, Jun; 1(1), pp. 63-72.
5. Stange , J.P. (2020), ‘Using resting-state intrinsic network connectivity to identify suicide risk in mood disorders’, Psychol Med, 50(14), pp. 2324-2334.
6. Chao-Gan, Y. (2010), ‘DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI’, Frontiers in Systems Neuroscience, vol. 4, art. 13, pp. 1-7.
7. Power, J.D. (2012), ‘Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion’, Neuroimage, vol 59, pp. 2142–2154.
8. Namaky, N. (2023), ‘Suicidal thoughts and behaviours among military veterans: protocol for a prospective, observational, neuroimaging study’, BMJ Open, 13(8):e070654.
9. Serafini, G. (2016), ‘Understanding suicidal behavior: the contribution of recent resting-state fMRI techniques’, Front Psychiatry, vol. 7, article 69, pp. 1-4.
10. Philippi, C.L. (2021), ‘Distinct patterns of resting-state connectivity in U.S. service members with mild traumatic brain injury versus posttraumatic stress disorder’, Brain Imaging Behav, 15(5), pp. 2616-2626